// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2018 Andy Davis <andydavis@google.com>
// Copyright (C) 2018 Eugene Zhulenev <ezhulenev@google.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#include "main.h"

#include <algorithm>
#include <set>

#include <Eigen/CXX11/Tensor>

using Eigen::ColMajor;
using Eigen::Index;
using Eigen::RowMajor;
using Eigen::Tensor;
using Eigen::internal::TensorBlockShapeType;

static TensorOpCost
zeroCost()
{
	return { 0, 0, 0 };
}

template<typename T>
static const T&
choose(int layout, const T& col, const T& row)
{
	return layout == ColMajor ? col : row;
}

static TensorBlockShapeType
RandomShape()
{
	return internal::random<bool>() ? TensorBlockShapeType::kUniformAllDims : TensorBlockShapeType::kSkewedInnerDims;
}

template<int NumDims>
static size_t
RandomTargetSize(const DSizes<Index, NumDims>& dims)
{
	return internal::random<size_t>(1, dims.TotalSize());
}

template<int NumDims>
static DSizes<Index, NumDims>
RandomDims()
{
	array<Index, NumDims> dims;
	for (int i = 0; i < NumDims; ++i) {
		dims[i] = internal::random<int>(1, 20);
	}
	return DSizes<Index, NumDims>(dims);
}

template<typename T>
static T*
GenerateRandomData(const Index& size)
{
	T* data = new T[size];
	for (int i = 0; i < size; ++i) {
		data[i] = internal::random<T>();
	}
	return data;
}

template<int NumDims>
static void
Debug(DSizes<Index, NumDims> dims)
{
	for (int i = 0; i < NumDims; ++i) {
		std::cout << dims[i] << "; ";
	}
	std::cout << std::endl;
}

template<int Layout>
static void
test_block_mapper_sanity()
{
	typedef internal::TensorBlockMapper<2, Layout> TensorBlockMapper;

	DSizes<Index, 2> tensor_dims(100, 100);

	// Test uniform blocks.
	TensorBlockMapper uniform_block_mapper(tensor_dims, { TensorBlockShapeType::kUniformAllDims, 100, zeroCost() });

	VERIFY_IS_EQUAL(uniform_block_mapper.blockCount(), 100);
	VERIFY_IS_EQUAL(uniform_block_mapper.blockTotalSize(), 100);

	// 10x10 blocks
	auto uniform_b0 = uniform_block_mapper.blockDescriptor(0);
	VERIFY_IS_EQUAL(uniform_b0.dimensions().at(0), 10);
	VERIFY_IS_EQUAL(uniform_b0.dimensions().at(1), 10);

	// Test skewed to inner dims blocks.
	TensorBlockMapper skewed_block_mapper(tensor_dims, { TensorBlockShapeType::kSkewedInnerDims, 100, zeroCost() });

	VERIFY_IS_EQUAL(skewed_block_mapper.blockCount(), 100);
	VERIFY_IS_EQUAL(skewed_block_mapper.blockTotalSize(), 100);

	// 1x100 (100x1) rows/cols depending on a tensor layout.
	auto skewed_b0 = skewed_block_mapper.blockDescriptor(0);
	VERIFY_IS_EQUAL(skewed_b0.dimensions().at(0), choose(Layout, 100, 1));
	VERIFY_IS_EQUAL(skewed_b0.dimensions().at(1), choose(Layout, 1, 100));
}

// Given a TensorBlock "visit" every element accessible though it, and a keep an
// index in the visited set. Verify that every coeff accessed only once.
template<int NumDims, int Layout>
static void
UpdateCoeffSet(const DSizes<Index, NumDims>& tensor_strides,
			   const internal::TensorBlockDescriptor<NumDims>& block,
			   Index first_coeff_index,
			   int dim_index,
			   std::set<Index>* visited_coeffs)
{
	const DSizes<Index, NumDims>& block_sizes = block.dimensions();

	for (int i = 0; i < block_sizes[dim_index]; ++i) {
		if (tensor_strides[dim_index] == 1) {
			typedef std::pair<std::set<Index>::iterator, bool> ReturnType;
			ReturnType inserted = visited_coeffs->insert(first_coeff_index + i);
			VERIFY_IS_EQUAL(inserted.second, true);
		} else {
			int next_dim_index = dim_index + choose(Layout, -1, 1);
			UpdateCoeffSet<NumDims, Layout>(tensor_strides, block, first_coeff_index, next_dim_index, visited_coeffs);
			first_coeff_index += tensor_strides[dim_index];
		}
	}
}

template<typename T, int NumDims, int Layout>
static void
test_block_mapper_maps_every_element()
{
	typedef internal::TensorBlockMapper<NumDims, Layout> TensorBlockMapper;

	DSizes<Index, NumDims> dims = RandomDims<NumDims>();
	DSizes<Index, NumDims> strides = internal::strides<Layout>(dims);

	// Keep track of elements indices available via block access.
	std::set<Index> coeff_set;

	// Try different combinations of block types and sizes.
	TensorBlockMapper block_mapper(dims, { RandomShape(), RandomTargetSize(dims), zeroCost() });

	for (int i = 0; i < block_mapper.blockCount(); ++i) {
		auto block = block_mapper.blockDescriptor(i);
		UpdateCoeffSet<NumDims, Layout>(strides, block, block.offset(), choose(Layout, NumDims - 1, 0), &coeff_set);
	}

	// Verify that every coefficient in the original Tensor is accessible through
	// TensorBlock only once.
	Index total_coeffs = dims.TotalSize();
	VERIFY_IS_EQUAL(Index(coeff_set.size()), total_coeffs);
	VERIFY_IS_EQUAL(*coeff_set.begin(), 0);
	VERIFY_IS_EQUAL(*coeff_set.rbegin(), total_coeffs - 1);
}

template<int Layout, int NumDims>
static Index
GetInputIndex(Index output_index,
			  const array<Index, NumDims>& output_to_input_dim_map,
			  const array<Index, NumDims>& input_strides,
			  const array<Index, NumDims>& output_strides)
{
	int input_index = 0;
	if (Layout == ColMajor) {
		for (int i = NumDims - 1; i > 0; --i) {
			const Index idx = output_index / output_strides[i];
			input_index += idx * input_strides[output_to_input_dim_map[i]];
			output_index -= idx * output_strides[i];
		}
		return input_index + output_index * input_strides[output_to_input_dim_map[0]];
	} else {
		for (int i = 0; i < NumDims - 1; ++i) {
			const Index idx = output_index / output_strides[i];
			input_index += idx * input_strides[output_to_input_dim_map[i]];
			output_index -= idx * output_strides[i];
		}
		return input_index + output_index * input_strides[output_to_input_dim_map[NumDims - 1]];
	}
}

template<int Layout, int NumDims>
static array<Index, NumDims>
ComputeStrides(const array<Index, NumDims>& sizes)
{
	array<Index, NumDims> strides;
	if (Layout == ColMajor) {
		strides[0] = 1;
		for (int i = 1; i < NumDims; ++i) {
			strides[i] = strides[i - 1] * sizes[i - 1];
		}
	} else {
		strides[NumDims - 1] = 1;
		for (int i = NumDims - 2; i >= 0; --i) {
			strides[i] = strides[i + 1] * sizes[i + 1];
		}
	}
	return strides;
}

template<typename Scalar, typename StorageIndex, int Dim>
class EqualityChecker
{
	const Scalar* input_data;
	const DSizes<StorageIndex, Dim>&input_dims, &input_strides, &output_dims, &output_strides;
	void check_recursive(const Scalar* input, const Scalar* output, int depth = 0) const
	{
		if (depth == Dim) {
			VERIFY_IS_EQUAL(*input, *output);
			return;
		}

		for (int i = 0; i < output_dims[depth]; ++i) {
			check_recursive(
				input + i % input_dims[depth] * input_strides[depth], output + i * output_strides[depth], depth + 1);
		}
	}

  public:
	EqualityChecker(const Scalar* input_data_,
					const DSizes<StorageIndex, Dim>& input_dims_,
					const DSizes<StorageIndex, Dim>& input_strides_,
					const DSizes<StorageIndex, Dim>& output_dims_,
					const DSizes<StorageIndex, Dim>& output_strides_)
		: input_data(input_data_)
		, input_dims(input_dims_)
		, input_strides(input_strides_)
		, output_dims(output_dims_)
		, output_strides(output_strides_)
	{
	}

	void operator()(const Scalar* output_data) const { check_recursive(input_data, output_data); }
};

template<int Layout>
static void
test_uniform_block_shape()
{
	typedef internal::TensorBlockDescriptor<5> TensorBlock;
	typedef internal::TensorBlockMapper<5, Layout> TensorBlockMapper;

	{
		// Test shape 'UniformAllDims' with uniform 'max_coeff count'.
		DSizes<Index, 5> dims(11, 5, 6, 17, 7);
		const Index max_coeff_count = 5 * 5 * 5 * 5 * 5;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		for (int i = 0; i < 5; ++i) {
			VERIFY_IS_EQUAL(5, block.dimensions()[i]);
		}
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	}

	// Test shape 'UniformAllDims' with larger 'max_coeff count' which spills
	// partially into first inner-most dimension.
	if (Layout == ColMajor) {
		DSizes<Index, 5> dims(11, 5, 6, 17, 7);
		const Index max_coeff_count = 7 * 5 * 5 * 5 * 5;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(7, block.dimensions()[0]);
		for (int i = 1; i < 5; ++i) {
			VERIFY_IS_EQUAL(5, block.dimensions()[i]);
		}
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	} else {
		DSizes<Index, 5> dims(11, 5, 6, 17, 7);
		const Index max_coeff_count = 5 * 5 * 5 * 5 * 6;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(6, block.dimensions()[4]);
		for (int i = 3; i >= 0; --i) {
			VERIFY_IS_EQUAL(5, block.dimensions()[i]);
		}
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	}

	// Test shape 'UniformAllDims' with larger 'max_coeff count' which spills
	// fully into first inner-most dimension.
	if (Layout == ColMajor) {
		DSizes<Index, 5> dims(11, 5, 6, 17, 7);
		const Index max_coeff_count = 11 * 5 * 5 * 5 * 5;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(11, block.dimensions()[0]);
		for (int i = 1; i < 5; ++i) {
			VERIFY_IS_EQUAL(5, block.dimensions()[i]);
		}
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	} else {
		DSizes<Index, 5> dims(11, 5, 6, 17, 7);
		const Index max_coeff_count = 5 * 5 * 5 * 5 * 7;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(7, block.dimensions()[4]);
		for (int i = 3; i >= 0; --i) {
			VERIFY_IS_EQUAL(5, block.dimensions()[i]);
		}
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	}

	// Test shape 'UniformAllDims' with larger 'max_coeff count' which spills
	// fully into first few inner-most dimensions.
	if (Layout == ColMajor) {
		DSizes<Index, 5> dims(7, 5, 6, 17, 7);
		const Index max_coeff_count = 7 * 5 * 6 * 7 * 5;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(7, block.dimensions()[0]);
		VERIFY_IS_EQUAL(5, block.dimensions()[1]);
		VERIFY_IS_EQUAL(6, block.dimensions()[2]);
		VERIFY_IS_EQUAL(7, block.dimensions()[3]);
		VERIFY_IS_EQUAL(5, block.dimensions()[4]);
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	} else {
		DSizes<Index, 5> dims(7, 5, 6, 9, 7);
		const Index max_coeff_count = 5 * 5 * 5 * 6 * 7;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(7, block.dimensions()[4]);
		VERIFY_IS_EQUAL(6, block.dimensions()[3]);
		VERIFY_IS_EQUAL(5, block.dimensions()[2]);
		VERIFY_IS_EQUAL(5, block.dimensions()[1]);
		VERIFY_IS_EQUAL(5, block.dimensions()[0]);
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	}

	// Test shape 'UniformAllDims' with full allocation to all dims.
	if (Layout == ColMajor) {
		DSizes<Index, 5> dims(7, 5, 6, 17, 7);
		const Index max_coeff_count = 7 * 5 * 6 * 17 * 7;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(7, block.dimensions()[0]);
		VERIFY_IS_EQUAL(5, block.dimensions()[1]);
		VERIFY_IS_EQUAL(6, block.dimensions()[2]);
		VERIFY_IS_EQUAL(17, block.dimensions()[3]);
		VERIFY_IS_EQUAL(7, block.dimensions()[4]);
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	} else {
		DSizes<Index, 5> dims(7, 5, 6, 9, 7);
		const Index max_coeff_count = 7 * 5 * 6 * 9 * 7;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(7, block.dimensions()[4]);
		VERIFY_IS_EQUAL(9, block.dimensions()[3]);
		VERIFY_IS_EQUAL(6, block.dimensions()[2]);
		VERIFY_IS_EQUAL(5, block.dimensions()[1]);
		VERIFY_IS_EQUAL(7, block.dimensions()[0]);
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	}
}

template<int Layout>
static void
test_skewed_inner_dim_block_shape()
{
	typedef internal::TensorBlockDescriptor<5> TensorBlock;
	typedef internal::TensorBlockMapper<5, Layout> TensorBlockMapper;

	// Test shape 'SkewedInnerDims' with partial allocation to inner-most dim.
	if (Layout == ColMajor) {
		DSizes<Index, 5> dims(11, 5, 6, 17, 7);
		const Index max_coeff_count = 10 * 1 * 1 * 1 * 1;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(10, block.dimensions()[0]);
		for (int i = 1; i < 5; ++i) {
			VERIFY_IS_EQUAL(1, block.dimensions()[i]);
		}
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	} else {
		DSizes<Index, 5> dims(11, 5, 6, 17, 7);
		const Index max_coeff_count = 1 * 1 * 1 * 1 * 6;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(6, block.dimensions()[4]);
		for (int i = 3; i >= 0; --i) {
			VERIFY_IS_EQUAL(1, block.dimensions()[i]);
		}
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	}

	// Test shape 'SkewedInnerDims' with full allocation to inner-most dim.
	if (Layout == ColMajor) {
		DSizes<Index, 5> dims(11, 5, 6, 17, 7);
		const Index max_coeff_count = 11 * 1 * 1 * 1 * 1;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(11, block.dimensions()[0]);
		for (int i = 1; i < 5; ++i) {
			VERIFY_IS_EQUAL(1, block.dimensions()[i]);
		}
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	} else {
		DSizes<Index, 5> dims(11, 5, 6, 17, 7);
		const Index max_coeff_count = 1 * 1 * 1 * 1 * 7;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(7, block.dimensions()[4]);
		for (int i = 3; i >= 0; --i) {
			VERIFY_IS_EQUAL(1, block.dimensions()[i]);
		}
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	}

	// Test shape 'SkewedInnerDims' with full allocation to inner-most dim,
	// and partial allocation to second inner-dim.
	if (Layout == ColMajor) {
		DSizes<Index, 5> dims(11, 5, 6, 17, 7);
		const Index max_coeff_count = 11 * 3 * 1 * 1 * 1;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(11, block.dimensions()[0]);
		VERIFY_IS_EQUAL(3, block.dimensions()[1]);
		for (int i = 2; i < 5; ++i) {
			VERIFY_IS_EQUAL(1, block.dimensions()[i]);
		}
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	} else {
		DSizes<Index, 5> dims(11, 5, 6, 17, 7);
		const Index max_coeff_count = 1 * 1 * 1 * 15 * 7;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(7, block.dimensions()[4]);
		VERIFY_IS_EQUAL(15, block.dimensions()[3]);
		for (int i = 2; i >= 0; --i) {
			VERIFY_IS_EQUAL(1, block.dimensions()[i]);
		}
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	}

	// Test shape 'SkewedInnerDims' with full allocation to inner-most dim,
	// and partial allocation to third inner-dim.
	if (Layout == ColMajor) {
		DSizes<Index, 5> dims(11, 5, 6, 17, 7);
		const Index max_coeff_count = 11 * 5 * 5 * 1 * 1;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(11, block.dimensions()[0]);
		VERIFY_IS_EQUAL(5, block.dimensions()[1]);
		VERIFY_IS_EQUAL(5, block.dimensions()[2]);
		for (int i = 3; i < 5; ++i) {
			VERIFY_IS_EQUAL(1, block.dimensions()[i]);
		}
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	} else {
		DSizes<Index, 5> dims(11, 5, 6, 17, 7);
		const Index max_coeff_count = 1 * 1 * 5 * 17 * 7;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(7, block.dimensions()[4]);
		VERIFY_IS_EQUAL(17, block.dimensions()[3]);
		VERIFY_IS_EQUAL(5, block.dimensions()[2]);
		for (int i = 1; i >= 0; --i) {
			VERIFY_IS_EQUAL(1, block.dimensions()[i]);
		}
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	}

	// Test shape 'SkewedInnerDims' with full allocation to all dims.
	if (Layout == ColMajor) {
		DSizes<Index, 5> dims(11, 5, 6, 17, 7);
		const Index max_coeff_count = 11 * 5 * 6 * 17 * 7;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(11, block.dimensions()[0]);
		VERIFY_IS_EQUAL(5, block.dimensions()[1]);
		VERIFY_IS_EQUAL(6, block.dimensions()[2]);
		VERIFY_IS_EQUAL(17, block.dimensions()[3]);
		VERIFY_IS_EQUAL(7, block.dimensions()[4]);
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	} else {
		DSizes<Index, 5> dims(11, 5, 6, 17, 7);
		const Index max_coeff_count = 11 * 5 * 6 * 17 * 7;
		TensorBlockMapper block_mapper(dims, { TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost() });
		TensorBlock block = block_mapper.blockDescriptor(0);
		VERIFY_IS_EQUAL(7, block.dimensions()[4]);
		VERIFY_IS_EQUAL(17, block.dimensions()[3]);
		VERIFY_IS_EQUAL(6, block.dimensions()[2]);
		VERIFY_IS_EQUAL(5, block.dimensions()[1]);
		VERIFY_IS_EQUAL(11, block.dimensions()[0]);
		VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
	}
}

template<int Layout>
static void
test_empty_dims(const internal::TensorBlockShapeType block_shape)
{
	// Test blocking of tensors with zero dimensions:
	//  - we must not crash on asserts and divisions by zero
	//  - we must not return block with zero dimensions
	//    (recipe for overflows/underflows, divisions by zero and NaNs later)
	//  - total block count must be zero
	{
		typedef internal::TensorBlockMapper<1, Layout> TensorBlockMapper;

		DSizes<Index, 1> dims(0);
		for (size_t max_coeff_count = 0; max_coeff_count < 2; ++max_coeff_count) {
			TensorBlockMapper block_mapper(dims, { block_shape, max_coeff_count, zeroCost() });
			VERIFY_IS_EQUAL(block_mapper.blockCount(), 0);
			VERIFY(block_mapper.blockTotalSize() >= 1);
		}
	}

	{
		typedef internal::TensorBlockMapper<2, Layout> TensorBlockMapper;

		for (int dim1 = 0; dim1 < 3; ++dim1) {
			for (int dim2 = 0; dim2 < 3; ++dim2) {
				DSizes<Index, 2> dims(dim1, dim2);
				for (size_t max_coeff_count = 0; max_coeff_count < 2; ++max_coeff_count) {
					TensorBlockMapper block_mapper(dims, { block_shape, max_coeff_count, zeroCost() });
					if (dim1 * dim2 == 0) {
						VERIFY_IS_EQUAL(block_mapper.blockCount(), 0);
					}
					VERIFY(block_mapper.blockTotalSize() >= 1);
				}
			}
		}
	}
}

#define TEST_LAYOUTS(NAME)                                                                                             \
	CALL_SUBTEST(NAME<ColMajor>());                                                                                    \
	CALL_SUBTEST(NAME<RowMajor>())

#define TEST_LAYOUTS_AND_DIMS(TYPE, NAME)                                                                              \
	CALL_SUBTEST((NAME<TYPE, 1, ColMajor>()));                                                                         \
	CALL_SUBTEST((NAME<TYPE, 1, RowMajor>()));                                                                         \
	CALL_SUBTEST((NAME<TYPE, 2, ColMajor>()));                                                                         \
	CALL_SUBTEST((NAME<TYPE, 2, RowMajor>()));                                                                         \
	CALL_SUBTEST((NAME<TYPE, 3, ColMajor>()));                                                                         \
	CALL_SUBTEST((NAME<TYPE, 3, RowMajor>()));                                                                         \
	CALL_SUBTEST((NAME<TYPE, 4, ColMajor>()));                                                                         \
	CALL_SUBTEST((NAME<TYPE, 4, RowMajor>()));                                                                         \
	CALL_SUBTEST((NAME<TYPE, 5, ColMajor>()));                                                                         \
	CALL_SUBTEST((NAME<TYPE, 5, RowMajor>()))

#define TEST_LAYOUTS_WITH_ARG(NAME, ARG)                                                                               \
	CALL_SUBTEST(NAME<ColMajor>(ARG));                                                                                 \
	CALL_SUBTEST(NAME<RowMajor>(ARG))

EIGEN_DECLARE_TEST(cxx11_tensor_block_access)
{
	TEST_LAYOUTS(test_block_mapper_sanity);
	TEST_LAYOUTS_AND_DIMS(float, test_block_mapper_maps_every_element);
	TEST_LAYOUTS(test_uniform_block_shape);
	TEST_LAYOUTS(test_skewed_inner_dim_block_shape);
	TEST_LAYOUTS_WITH_ARG(test_empty_dims, TensorBlockShapeType::kUniformAllDims);
	TEST_LAYOUTS_WITH_ARG(test_empty_dims, TensorBlockShapeType::kSkewedInnerDims);
}

#undef TEST_LAYOUTS
#undef TEST_LAYOUTS_WITH_ARG
